{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T17:59:34Z","timestamp":1762192774957,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62402124"],"award-info":[{"award-number":["62402124"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Project Program of Guangxi Key Laboratory of Digital Infrastructure","award":["GXDINBC202402","GXDINBC202407"],"award-info":[{"award-number":["GXDINBC202402","GXDINBC202407"]}]},{"name":"Guangxi Science and Technology Base and Talent Project","award":["GuikeAD23026160"],"award-info":[{"award-number":["GuikeAD23026160"]}]},{"DOI":"10.13039\/100012547","name":"Guangxi Natural Science Foundation","doi-asserted-by":"publisher","award":["2025GXNSFBA069283"],"award-info":[{"award-number":["2025GXNSFBA069283"]}],"id":[{"id":"10.13039\/100012547","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The proliferation of sophisticated malware poses a persistent threat to cybersecurity. While visualizing malware as images enables the use of Convolutional Neural Networks, standard architectures are often inefficient and struggle with the high spatial and channel redundancy inherent in these representations. To address this challenge, we propose LR-MalConv, a new detection framework centered on a novel Low-Redundancy Convolution (LR-Conv) module. The LR-Conv module is uniquely designed to synergistically reduce both spatial redundancy, via a gating and reconstruction mechanism, and channel redundancy, through an efficient split\u2013transform\u2013fuse strategy. By integrating LR-Conv into a ResNet backbone, our framework enhances discriminative feature extraction while significantly reducing computational overhead. Extensive experiments on the Malimg benchmark dataset show our method achieves an accuracy of 99.52%, outperforming existing methods. LR-MalConv establishes a new benchmark for visualized malware detection by striking a superior balance between accuracy and computational efficiency, demonstrating the significant potential of redundancy reduction in this domain.<\/jats:p>","DOI":"10.3390\/computers14110470","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T17:32:01Z","timestamp":1762191121000},"page":"470","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Detecting Visualized Malicious Code Through Low-Redundancy Convolution"],"prefix":"10.3390","volume":"14","author":[{"given":"Xiao","family":"Liu","sequence":"first","affiliation":[{"name":"Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing, School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiawang","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing, School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingying","family":"Ren","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing, School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jining","family":"Chen","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Digital Infrastructure, Guangxi Zhuang Autonomous Region Information Center, Nanning 530000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"639","DOI":"10.3233\/JCS-2010-0410","article-title":"Automatic analysis of malware behavior using machine learning","volume":"19","author":"Rieck","year":"2011","journal-title":"J. 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